Bibliographic record
Abstract
Cities have been the engines of economic growth since the industrial revolution. While effective at catalyzing prosperity, city development has not always been “smart” sacrificing human health, for instance, for greater productivity. Smart cities are now emerging. Leading smart cities such as Stockholm, Barcelona, New York, Vienna, and Toronto have incorporated efficiency into buildings, infrastructure, and social spaces using technological advancements, increasing the livability, workability, and sustainability of these places. Inspired by these smart city developments, India is planning to build 100 smart cities in various parts of the country. This research presents insight into how smart cities are likely to evolve in India, by studying the priority areas considered in planning smart cities. It presents both the citizen and city official perspectives of smart cities. The results indicate that citizens value living, followed by mobility, environment, governance, and economy, whereas the city officials prioritize living, followed by environment, economy, mobility, and governance. This research further evaluated the titles of planned smart city projects to determine how many of them can be categorized as smart. The analysis also revealed how city size influences the priorities of citizens and city officials, indicating that the notion of a smart city in India may be context specific.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".